0.1 Principal component analysis of all samples

 

0.2 Differential gene expression analysis in cNF vs icNF samples:

TMM normalization was used to prepare the dataset for differential gene expression analysis. We then used limma-edgeR based analysis to find differentially expressed genes in the TMM normalized dataset.

 

The volcano plot below shows genes that are significantly overexpressed in cNF compared to icNF (logFC > 2) in red. The dots in blue refer to genes that are significantly underexpressed in icNF compared to cNF (logFC < -2). The thresholds of fold change have been arbitrarily chosen for ease of visualization in the volcano plot shown below.

The heatmap below the volcano plot shows the expression differences of the top 100 differentially expressed genes between cNF and icNF samples. The cluster tree shows splitting of the differentially expressed genes into 2 main clusters according to expression patterns in the comparison groups.

 

0.2.1 Pathway analysis

The clusters of genes identified above were then subjected to pathway analysis using gprofiler2 which uses hypergeometric test to examine enrichment of particular cellular pathways in the gene lists provided.

The Manhattan plots show the enrichment scores (logPvalues) for pathways in various databases (e.g. KEGG, REAC, GO:MF, GO:CC etc) that represent the genes in each of the identified clusters (Clusters 1, 2, and 3).

The table below each Manhattan plot shows the list of KEGG, REAC, and WP pathways enriched in that specific cluster along with the pValue of enrichment.

## [1] "Enrichment Plot of Cluster 1 genes"
## This version of bslib is designed to work with rmarkdown version 2.7 or higher.

## [1] "Enrichment Plot of Cluster 2 genes"

## [1] "Enrichment Plot of Cluster 3 genes"

## [1] "Enrichment Plot of Cluster 4 genes"

 

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.